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Excelھ sales forecasting for dummies
A guide to using Microsoft Excel to organize and forecast sales data.
Book
Comparative Analysis of SARIMA, Prophet, and a Diagnostic Decomposition–Correction Hybrid for Long-Horizon Lottery Sales Forecasting
Accurate forecasting of lottery sales is crucial for strategic planning in volatile consumer markets driven by trend shifts, multi-scale seasonality, and calendar effects. This study proposes a Diagnostic Decomposition–Correction Hybrid (DDC-Hybrid) framework integrating Prophet and SARIMA through a residual diagnostics and correction pipeline. Specifically, Prophet is employed to model long-term trend changes and interpretable holiday impacts, while SARIMA is subsequently used to correct the residual series, capturing short-range temporal dependence that remains statistically significant after decomposition. From an information-theoretic perspective, the framework can be viewed as a two-stage uncertainty reduction process, where decomposition extracts low-frequency informative components and residual correction harvests remaining predictive information. Using monthly lottery sales in China (2008–2025), we conduct a comprehensive evaluation of SARIMA, Prophet, and the proposed hybrid approach. The DDC-Hybrid demonstrates improved predictive accuracy, yielding the lowest error rates. Beyond predictive accuracy, we further examine varying holiday effects through statistical testing. We also find that lottery sales contain a pronounced quadrennial (48-month) seasonal cycle associated with mega-sport events, which improves long-horizon stability. The results suggest that the proposed diagnostic hybrid modeling approach enhances forecasting accuracy and provides practical insights for lottery sales management.
Journal Article
Aggregate Sales Forecasting Based on Spatial Correlation in Retail
Aggregated sales forecasting is an important research topic in the highly competitive retail industry. With the availability of different data sources, various techniques and models have been proposed for aggregated sales forecasting. However, existing methods often overlook the spatial correlation in sales between neighboring retailers. In this paper, we propose a new framework for aggregated sales forecasting based on the deep learning technique ConvLSTM with an attention mechanism to solve this challenge. In the new framework, ConvLSTM is utilized to fully leverage spatially relevant information from adjacent retailers, while the attention mechanism is employed to capture spatial dependencies and select the most pertinent data from spatial inputs. Furthermore, a spatial sliding window technique is designed to augment the sample size. To validate the efficacy of our proposed framework, we conducted experiments using real-world retail sales data and compared our model with established benchmarks. Additionally, we conducted an ablation study to assess the contributions of key components, including the attention mechanism and spatial data augmentation. The experimental results demonstrate that our proposed model effectively improves the prediction performance, offering a novel approach to aggregate sales forecasting for both industry and academia.
Journal Article
Hyperautomation on fuzzy data dredging on four advanced industrial forecasting models to support sustainable business management
Recently, traditional manufacturing industries have faced two serious gaps and problems in line with effective product-line sales forecasting methods to balance the negative impacts on the performance of the subjective experience, including (1) arbitrary judgment, such as growth rate of expectancy, manager’s experiences, and historical sales data, may cause inaccurately predictive results and severe negative effects, and (2) sales forecasting is a key priority and challenge in the context of considerable product lines that have different properties and need specific models for supporting decision analytics. This study is motivated to propose an advanced hybrid model to utilize the advantages of variation for methods of fuzzy time series (FTS), exponential smoothing (ES), moving average (MA), and regression analysis (RA). To analyze the four product lines—stably growing product (SGP), declining product (DP), irregularly growing product (IGP), and special sales product (SSP)—this study is based on empirical sales data from a leading traditional manufacturer to accurately identify the high potentials of decisive key factors and objectively evaluate the model. Two evaluation standards—the mean absolute percentage error (MAPE) and root mean square error (RMSE), a parameter sensitivity analysis, and comparative analysis—are measured. After implementing the data from the case study, four key reports were conclusively identified. (1) Purely for the RMSE, the best one (10.32) is the ES method in the SGP line. (2) In the DP line, the better one is the RA(2) method, with a relatively low MAPE of 17.78% and RMSE of 26.46. (3) The FTS method is the best choice (MAPE 12.41% and RMSE 18.98) for the IGP line. (4) For the SSP line, the better one (MAPE 24.05 and RMSE 29.34) is the MA method. According to the above reports, although the proposed hybrid model has a general performance for the SSP line, it still has a superior predictor when compared to manager subjective prediction. Interestingly, the proposed model is rarely used, has a new trial with an innovative solution for the traditional manufacturer, and thus realizes applicable values. The study concludes with acceptable and satisfactory results and yields seven important findings and three managerial implications that significantly contribute to decision-making reference for complete sales-production planning for interested parties. Thus, this study benefits and values a conventional industry upgrade from novel application techniques.
Journal Article
Prediction Analysis for Business To Business (B2B) Sales of Telecommunication Services using Machine Learning Techniques
Sales prediction analysis requires intelligent data mining techniques with accurate prediction models and high reliability. In most cases, business highly relies on information as well as demand forecast of the sales trends. This research uses B2B sales data for analysis. The B2B data could provide information on how telecommunication company should manage its sales team, products, and budgeting flows. The accurate estimates enable Telecommunication company to survive the market war and increase with market growth. Comprehensible predictive models were studied and analyzed using a technique of machine learning to improve the prediction of the future sale. It is hard to cope with big data and sale prediction accuracy if the system of traditional forecast is used. In this study, machine learning technique was also used to analyze the reliability of B2B sales. In addition, at the end of this research, other measures and techniques used to predict sales were introduced. The predictive model with best performance evaluation is recommended to forecast the trending B2B sales. The study results are put into an order of reliability and accuracy of the best method to predict and forecast including estimation, evaluation, and transformation. The best performance model found was Gradient Boost Algorithm. The result form graph the data close together from beginning till end of data target MSE and MAPE result are the best result than other method, MSE =24.743.000.000,00 and MAPE =0,18. This model performed maximum accuracy in predicting and forecasting of the future B2B sales.
Journal Article
Analysis of Factors Affecting Product Sales with an Outlook toward Sale Forecasting in Cosmetic Industry using Statistical Methods
There are several factors associated with the sale of cosmetic products which contribute to gaining market share for related companies in this industry. Furthermore, sales forecasting is indispensable in all levels of a company’s supply chain including production, distribution and logistics, marketing, and sale. This article mainly focuses on the analysis of characteristics affecting sales and sales forecasting in the cosmetics industry in which it will be helpful in determining sales strategies of cosmetics companies. Therefore, as a case study in this study, the main factors affecting the sale of cosmetic products were determined and categorized; accordingly. Three products including moisturizing cream, perfume, and sunscreen were examined using a statistical method. The effect of factors on product sales was predicted using the spline smooth prediction method and based on the predicted values, using the non-parametric Friedman test and Mean Rank, the effective factors were ranked in each of the three products. Moreover, the company’s sales volume in each of the three products was forecasted by using ARIMA models. The results demonstrated that factors such as “price” and “product” elements are the main drivers influencing the sales of moisturizing creams and “promotion” and “Inflation rate” factors play the most effective role in the sales of the perfume. Also, the compound aggregated growth rate (CAGR) for moisturizers, perfumes, and sunscreens over a five-year period in the study company are 30%, 29%, and 45%, respectively. It is very clear that to achieve ideal sales, paying attention to these influential factors and forecasting product sales lead to predicting material procurement of manufactures, distribution channels, and sales which finally provides business with customer satisfaction.
Journal Article
A Residual-Corrected Hybrid ARIMA–CNN–LSTM Framework for High-Accuracy Tobacco Sales Forecasting in Regulated Markets
As a common consumer product threatening public health, tobacco not only hinders the development of national public health, but also plays a significant impact on the national economy. The ARIMA model is reliable in learning linear or regular relationships, while the deep learn, such as convolutional neural network (CNN) and long short-term memory network (LSTM), is superior when capturing and learning nonlinear relationships. Combining time-series forecasting models with deep learning technologies, the hybrid architecture could integrate advantages and optimize forecasting effect. In this paper, leveraging 2023 daily sales data from a Southern Chinese tobacco company, this study proposes a new hybrid deep learning framework that integrates ARIMA, CNN, and LSTM models to address these inherent limitations and enhance prediction accuracy. This architecture decomposes forecasting tasks into linear trend analysis and nonlinear residual learning. The ARIMA component learns the linear relationship, and the CNN–LSTM component plays the role in the residual-driven correction. They enable synergistic capture of temporal dependencies and localized anomalies and enhancing the fitting effect. This hybrid model's optimization primarily relies on the residual-driven correction mechanism in the CNN–LSTM component, which significantly enhanced the model interpretability (
R
2
: 0.95, enhance 10.5% compare with ARIMA model, enhance 13.1% compare with CNN-LSTM model). This research not only advances hybrid deep learning methods, but also provides a scalable solution for precise predictions in dynamic markets. This excellent forecasting results could also be practiced in inventory optimization and policy impact studies.
Journal Article
Decision-making framework with double-loop learning through interpretable black-box machine learning models
Purpose
The purpose of this paper is to address the problem of weak acceptance of machine learning (ML) models in business. The proposed framework of top-performing ML models coupled with general explanation methods provides additional information to the decision-making process. This builds a foundation for sustainable organizational learning.
Design/methodology/approach
To address user acceptance, participatory approach of action design research (ADR) was chosen. The proposed framework is demonstrated on a B2B sales forecasting process in an organizational setting, following cross-industry standard process for data mining (CRISP-DM) methodology.
Findings
The provided ML model explanations efficiently support business decision makers, reduce forecasting error for new sales opportunities, and facilitate discussion about the context of opportunities in the sales team.
Research limitations/implications
The quality and quantity of available data affect the performance of models and explanations.
Practical implications
The application in the real-world company demonstrates the utility of the approach and provides evidence that transparent explanations of ML models contribute to individual and organizational learning.
Social implications
All used methods are available as an open-source software and can improve the acceptance of ML in data-driven decision making.
Originality/value
The proposed framework incorporates existing ML models and general explanation methodology into a decision-making process. To the authors’ knowledge, this is the first attempt to support organizational learning with a framework combining ML explanations, ADR, and data mining methodology based on the CRISP-DM industry standard.
Journal Article
Short-Term and Medium-Term Electricity Sales Forecasting Method Based on Deep Spatio-Temporal Residual Network
The forecasting of electricity sales is directly related to the power generation planning of power enterprises and the progress of the generation tasks. Aiming at the problem that traditional forecasting methods cannot properly deal with the actual data offset caused by external factors, such as the weather, season, and spatial attributes, this paper proposes a method of electricity sales forecasting based on a deep spatio-temporal residual network (ST-ResNet). The method not only relies on the temporal correlation of electricity sales data but also introduces the influence of external factors and spatial correlation, which greatly enhances the fitting degree of each parameter of the model. Moreover, the residual module and the convolution module are fused to effectively reduce the damage of the deep convolutional process to the training effectiveness. Finally, the three comparison experiments of the ultra-short term, short term and medium term show that the MAPE forecasted by the ST-ResNet model is at least 2.69% lower than that of the RNN and other classical Deep Learning models, that its RMSE is at least 36.2% lower, and that its MAD is at least 34.2% lower, which is more obvious than the traditional methods. The effectiveness and feasibility of the ST-ResNet model in the short-term forecasting of electricity sales are verified.
Journal Article
Bi-GRU-APSO: Bi-Directional Gated Recurrent Unit with Adaptive Particle Swarm Optimization Algorithm for Sales Forecasting in Multi-Channel Retail
In the present scenario, retail sales forecasting has a great significance in E-commerce companies. The precise retail sales forecasting enhances the business decision making, storage management, and product sales. Inaccurate retail sales forecasting can decrease customer satisfaction, inventory shortages, product backlog, and unsatisfied customer demands. In order to obtain a better retail sales forecasting, deep learning models are preferred. In this manuscript, an effective Bi-GRU is proposed for accurate sales forecasting related to E-commerce companies. Initially, retail sales data are acquired from two benchmark online datasets: Rossmann dataset and Walmart dataset. From the acquired datasets, the unreliable samples are eliminated by interpolating missing data, outlier’s removal, normalization, and de-normalization. Then, feature engineering is carried out by implementing the Adaptive Particle Swarm Optimization (APSO) algorithm, Recursive Feature Elimination (RFE) technique, and Minimum Redundancy Maximum Relevance (MRMR) technique. Followed by that, the optimized active features from feature engineering are given to the Bi-Directional Gated Recurrent Unit (Bi-GRU) model for precise retail sales forecasting. From the result analysis, it is seen that the proposed Bi-GRU model achieves higher results in terms of an R2 value of 0.98 and 0.99, a Mean Absolute Error (MAE) of 0.05 and 0.07, and a Mean Square Error (MSE) of 0.04 and 0.03 on the Rossmann and Walmart datasets. The proposed method supports the retail sales forecasting by achieving superior results over the conventional models.
Journal Article